Building State Capacity: Evidence from Biometric Smartcards in India†

American Economic Review 2016, 106(10): 2895–2929 http://dx.doi.org/10.1257/aer.20141346 Building State Capacity: Evidence from Biometric Smartcards in India† By Karthik Muralidharan, Paul Niehaus, and Sandip Sukhtankar* Antipoverty programs in developing countries are often difficult to implement; in particular, many governments lack the capacity to deliver payments securely to targeted beneficiaries. We evaluate the impact of biometrically authenticated payments infrastructure “Smartcards” on beneficiaries of employment NREGS and pen- sion( SSP programs) in the Indian state of Andhra( Pradesh) , using a large-scale( ) experiment that randomized the rollout of Smartcards over 157 subdistricts and 19 million people. We find that, while incom- pletely implemented, the new system delivered a faster, more predict- able, and less corrupt NREGS payments process without adversely affecting program access. For each of these outcomes, treatment group distributions first-order stochastically dominated those of the control group. The investment was cost-effective , as time savings to NREGS beneficiaries alone were equal to the cost of the intervention, and there was also a significant reduction in the “leakage” of funds between the government and beneficiaries in both NREGS and SSP programs. Beneficiaries overwhelmingly preferred the new system for both programs. Overall, our results suggest that investing in secure payments infrastructure can significantly enhance “state capacity” to implement welfare programs in developing countries. JEL H53, H55, I32, I38, J65 ( ) * Muralidharan: Department of Economics, University of California, San Diego, 9500 Gilman Drive, La Jolla, CA 92093 e-mail: [email protected] ; Niehaus: Department of Economics, University of California, San Diego, 9500 Gilman( Drive, La Jolla, CA )92093 e-mail: [email protected] ; Sukhtankar: Department of Economics, University of Virginia, Monroe Hall, 248( McCormick Road, Charlottesville,) VA 22903 e-mail: [email protected] . We thank Santosh Anagol, Abhijit Banerjee, Julie Cullen, Gordon Dahl,( Roger Gordon, Rema Hanna, Gordon Hanson,) Erzo Luttmer, Santhosh Mathew, Simone Schaner, Monica Singhal, Anh Tran, and several seminar participants for comments. We are grateful to officials of the Government of Andhra Pradesh, including Reddy Subrahmanyam, Koppula Raju, Shamsher Singh Rawat, Raghunandan Rao, G. Vijaya Laxmi, AVV Prasad, Kuberan Selvaraj, Sanju, Kalyan Rao, and Madhavi Rani; as well as Gulzar Natarajan for their continuous support of the Andhra Pradesh Smartcard Study. We thank officials of the Unique Identification Authority of India UIDAI , including Nandan Nilekani, Ram Sevak Sharma, and R. Srikar for their support, and Tata Consultancy( Services) TCS and Ravi Marri, Ramanna, and Shubra Dixit for their help in providing us with administrative data. This paper( )would not have been possible without the outstanding efforts and inputs of the J-PAL IPA project team, including Vipin Awatramani, Kshitij Batra, Prathap Kasina, Piali Mukhopadhyay, Michael Kaiser,/ Raghu Kishore Nekanti, Matt Pecenco, Surili Sheth, and Pratibha Shrestha. We are deeply grateful to the Omidyar Network—especially Jayant Sinha, C. V. Madhukar, Surya Mantha, Ashu Sikri, and Dhawal Kothari—for the financial support and long-term commitment that made this study possible. We also thank IPA, Yale University, and the Bill and Melinda Gates Foundation for additional financial support through the Global Financial Inclusion Initiative. The authors declare that they have no relevant or material financial interests that relate to the research described in this paper. † Go to http://dx.doi.org/10.1257/aer.20141346 to visit the article page for additional materials and author disclosure statement s . ( ) 2895 2896 THE AMERICAN ECONOMIC REVIEW octoBER 2016 Developing countries spend billions of dollars annually on antipoverty programs, but the delivery of these programs is often poor and plagued by high levels of corruption World Bank 2003; Pritchett 2010 . It is therefore plausible that investing ( ) in state capacity for better program delivery may have high returns. Yet, while the importance of state capacity for economic development has been emphasized in recent theoretical work Besley and Persson 2009, 2010 , there is limited empirical ( ) evidence on the returns to such investments. One frequent constraint on effective program implementation is the lack of a secure payments infrastructure to make transfers to intended beneficiaries. Money meant for the poor is often simply stolen by officials along the way, with case studies estimating “leakage” of funds as high as 70 to 85 percent Reinikka and Svensson ( 2004; Programme Evaluation Organisation 2005; Niehaus and Sukhtankar 2013b . ) Thus, building a secure payments infrastructure, which makes it easier for governments to accurately identify beneficiaries and transfer benefits directly into their bank accounts, may significantly improve state capacity for program implementation.1 This view has gained momentum from recent technological advances, which have made it feasible to issue payments via bank accounts linked to biometrically authenticated unique IDs. Biometric technology is seen as especially promising in developing countries, where high illiteracy rates make it unrealistic to universally deploy traditional forms of authentication, such as passwords or personal identification numbers PINs .2 The potential for such payment systems to improve the per- ( ) formance of public welfare programs and also increase financial inclusion for the ( poor has generated enormous global interest, with at least 230 programs in over 80 ) countries deploying biometric identification and payment systems Gelb and Clark ( 2013 . This enthusiasm is exemplified by India’s ambitious Aadhaar initiative to ) provide biometric-linked unique IDs UIDs to nearly one billion residents, and then ( ) transition social program payments to Direct Benefit Transfers viaUID-linked bank accounts. Over 850 million UIDs had been issued as of June 2015, with the former Finance Minister of India claiming that the project would be “a game changer for governance” Harris 2013 . ( ) At the same time, there are a number of reasons to be skeptical about the hype around these new payment systems. First, their implementation entails solving a complex mix of technical and logistical challenges, raising the concern that the undertaking might fail unless all components are well implemented Kremer 1993 . ( ) Second, vested interests whose rents are threatened may subvert the intervention and limit its effectiveness Krusell and Ríos-Rull 1996; Parente and Prescott 2000 . ( ) Third, the new system could generate exclusion errors if genuine beneficiaries are denied payments due to technical problems. This would be particularly troubling if it disproportionately hurt the most vulnerable beneficiaries Khera 2011 . Fourth, ( ) reducing corruption on some margins could displace it onto others e.g., Yang 2008a ( ) or could paradoxically hurt the poor if it dampened incentives for officials to implement antipoverty programs in the first place Leff 1964 . Finally, even assuming ( ) 1 It may also expand the state’s long-term choice set of policies that are feasible to implement, including replac- ing distortionary commodity subsidies with equivalent income transfers. 2 Fujiwara 2015 provides analogous evidence from Brazil on the effectiveness of electronic voting technology in circumventing( literacy) constraints, and on increasing enfranchisement of less-educated voters. VOL. 106 NO. 10 MURALIDHARAN ET AL.: BUILDING STATE CAPACITY 2897 positive impacts, cost-effectiveness is unclear as the best available estimates depend on a number of untested assumptions see e.g., National Institute for Public Finance ( and Policy 2012 . Overall, there is very limited evidence to support either the enthu- ) siasts or the skeptics of biometric payment systems. In this paper, we contribute toward filling this gap, by presenting evidence from a large-scale experimental evaluation of the impact of rolling out biometric payments infrastructure to make social welfare payments in India. Working with the Government of the Indian state of Andhra Pradesh AP ,3 we randomized the order ( ) in which 157 subdistricts introduced a new Smartcard initiative for making payments in two large welfare programs: the National Rural Employment Guarantee Scheme NREGS , and Social Security Pensions SSP . NREGS is the largest work- ( ) ( ) fare program in the world targeting 800 million rural residents in India , but has ( ) well-known implementation issues including problems with the payment process and leakage Dutta et al. 2012; Niehaus and Sukhtankar 2013a,b . SSP programs ( ) complement NREGS by providing income support to the rural poor who are not able to work Dutta, Murgai, and Howes 2010 . The new Smartcard-based payment sys- ( ) tem used a network of locally hired, bank-employed staff to biometrically authenti- cate beneficiaries and make cash payments in villages. It thus provided beneficiaries of NREGS and SSP programs with the same effective functionality as intended by UID-linked Direct Benefit Transfers. The experiment randomized the rollout of Smartcards across 157 subdistricts covering some 19 million people. Randomizing at this scale lets us address one common concern about randomized trials in developing countries: that studying small-scale pilots especially when non-governmental organization-led

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